Dataset

Our Google Street View dataset contains 62,058 high quality Google Street View images. The images cover the downtown and neighboring areas of Pittsburgh, PA; Orlando, FL and partially Manhattan, NY. Accurate GPS coordinates of the images and their compass direction are provided as well.
For each Street View placemark (i.e. each spot on one street), the 360° spherical view is broken down into 4 side views and 1 upward view. There is one additional image per placemark which shows some overlaid markers, such as the address, name of streets, etc.
The above figure shows sample street view images belonging to eight place marks of the dataset on the left. Sixteen sample user uploaded images which were used as query images in the related paper are shown on the right.

Naming format

The name of the images has the following format: XXXXXX_Y.jpg
XXXXXX is the identifier of the placemark. There are total number of 10343 placemarks in this dataset, so XXXXXX ranges from 000001 to 10343.
Y is the identifier of the view. 1, 2, 3 and 4 are the side views and 5 is the upward view. 0 is the view with markers overlaid (explained above). Thus, there are total number of 6 images per placemark.

GPS Coordinates & Compass Direction

The Matlab file 'GPS_Long_Lat_Compass.mat' includes the GPS coordinates and compass direction of each placemark. The row number XXXXXX corresponds to the placemark number XXXXXX.The 1st and 2nd columns are the latitude and longitude values. The 3rd column is thecompass direction (in degrees from North towards West) of the view number 4. The rest of the side views are exactly 90° apart from the view number 4.
The file 'Cartesian_Location_Coordinates.mat' contains the location coordinates in a metric Cartesian system (unlike longitude and latitude). The Euclidean distance between such XYZ coordinates of two points is the actual distance (in meters) between them.

GIST & Color Histogram

The file 'GIST.mat' includes precomputed GIST features of the images. The file 'Color_hist.mat' contains the 60 dimensional RGB color histogram of the images (20 dimensional histogram per channel). In each of these files, the row number XXXXXXY corresponds to the image XXXXXX_Y.jpg.

Citation

If you use the dataset, please cite the following paper for which this data was collected (partially):
Amir Roshan Zamir and Mubarak Shah, "Image Geo-localization Based on Multiple Nearest Neighbor Feature Matching using Generalized Graphs",
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2014 [PDF | BibTeX ]

Related Publications

Also, take a look at a few of our other papers and projects in the area of geo-spatial analysis of images and videos: